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A case study for an estimation of carbon fixation capacity in the mangrove plantation of Rhizophora apiculata trees in Trat, Thailand

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A case study for an estimation of carbon fixation capacity in the mangrove plantation of Rhizophora apiculata trees in Trat, Thailand
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  A case study for an estimation of carbon fixation capacity in themangrove plantation of   Rhizophora apiculata  trees in Trat, Thailand Yosuke Okimoto a,b, ⇑ , Akihiro Nose a , Kenzo Oshima a , Yutaka Tateda c , Takashi Ishii c a Faculty of Agriculture, Saga University, Honjyo-machi 1, Saga 840-8502, Japan b Center for International Forestry Research, Jalan CIFOR, Situ Gede, Bogor Barat 16115, Indonesia c Environmental Science Research Laboratory, Central Research Institute of Electronic Power Industry, Abiko 1646, Chiba 270-1194, Japan a r t i c l e i n f o  Article history: Received 1 May 2013Received in revised form 23 August 2013Accepted 24 August 2013Available online 20 October 2013 Keywords: Global warmingGrowth curveCoastal degradationGas exchangePhotosynthesisRespiration a b s t r a c t In order to accurately evaluate the contribution of mangrove forests in reducing the effects of climatechange, a precise understanding of the carbon fixation capacity of mangrove trees is needed. However,a fully reliable method to estimate carbon fixation capacity has yet to be established. In this study, netcarbonfixationofarepresentativemangrovetreeinSouth-EastAsia, Rhizophora apiculata ,wasestimated.It was calculatedwithtwodifferent procedures; thegasexchangeanalysisandthegrowthcurveanalysismethods. The gas exchange analysis method is based on calculated carbon values of the differencebetween photosynthetic absorption and respiratory emission. These two parameters were calculatedby using photosynthetic rates of single-leaves in response to light and temperature and respiratory ratesof trunkandbranchinresponsetotemperature. Thesemonthlyvalueswereadjustedwithmonthlyaver-age measurements of light intensity and temperature to improve estimation accuracy. The value of annual net carbon fixation for 3, 4, 5 and 9year-old forests was estimated to be 2.5–30.5MgCha  1 yr  1 .The values with the temperature modification increased by 9.3–21.3%, compared to those of 2.2–25.2MgCha  1 yr  1 without the temperature modification. However, it was found that these esti-mated values were significantly higher than the results produced by the growth curve analysis method,which produced 1.1–35.2MgCha  1 yr  1 . Results of this study show that further work is required toimprove the estimation accuracy for both the gas exchange analysis and growth curve analysis methods.There is a particular need to take into account the respiratory carbon emissions from the plant root forthe former method and determination of the maximum biomass at the mature tree phase for the lattermethod.   2013 Elsevier B.V. All rights reserved. 1. Introduction Massive losses inmangroveforest cover have occurredinSouthEastAsiaoverthelast fewdecades. InThailand, thecoverwas esti-mated as 372  10 3 ha in 1960 but decreased to 142  10 3 ha by1989. In 1997, the Thai Royal Forest Department (RFD) promoteda mangrove rehabilitation program (Manassrisuksi et al., 2001)and the area have recovered to 244  10 3 ha in 2000 (FAO, 2004).A critically important function of forest is to sequester andaccumulate great amounts of the greenhouse gas carbon dioxide(CO 2 ), which is of primary significance in attempts to address theeffects of global climate change. Afforestation and reforestation(AR)-clean development mechanism (CDM) is one of the primecountermeasures and the process of reducing emissions fromdeforestation in developing countries, incorporating conservation,sustainable management and enhancement of forest carbonstocks(REDD plus) has been one of the most controversial issues in theclimate change debate.Mangrove trees have a higher carbonfixationcapacitythanter-restrial forests (Lugo and Snedaker, 1974; Mann, 1982; Chmuraet al., 2003; Bouillon et al., 2008; Donato et al., 2011). The man-groves together with salt marshes and sea grasses form is referredto as the earth’s ‘blue carbon sinks’, which capture and store be-tween 235 and 450 trillion tons of carbon every year (Nellemannet al., 2009). Of great interest is the potential value of mangroves’in carbon mitigation programs such as REDD+.However, when accounting for mangrove biomass, only tenequations in the five reports are approved by the United NationsFramework Convention on Climate Change (UNFCCC) (Putz andChan, 1986; Day et al., 1987; Clough and Scott, 1989; Chaveet al., 2005; Smith and Whelan, 2006). The allometric method isaccordance with standard empirical relationships of tree growthsuch as diameter at breast height (DBH; 1.3m from the ground),tree height and tree biomass. However, it requires laborious and 0378-1127/$ - see front matter    2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.foreco.2013.08.051 ⇑ Corresponding author at: Center for International Forestry Research, JalanCIFOR, Situ Gede, Bogor Barat 16115, Indonesia. E-mail address:  yosuke_okimoto@hotmail.com (Y. Okimoto).Forest Ecology and Management 310 (2013) 1016–1026 Contents lists available at ScienceDirect Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco  long-term measurements in the field, and which is not sufficientfor estimation accuracy. These are the most significant reasonsrequiringthedevelopmentofnewalternativeassessmentmethods(Sedjo and Marland, 2003).One method for estimating the net ecosystem production(NEP) is, as reported by Komiyama et al. (2008), through the eddycovariance method. However, this method is unsatisfactory, as itrequires the physical deployment of expensive scientific equip-ment within mangrove forests, and the use of complex computa-tional procedures (Monji et al., 2002). In addition, remote sensingprovides options for continuous monitoring of mangrove overtime (Diorio et al., 2007), but the present study focuses on theestablishment of an accurate classification method that enablesdiscrimination between mangrove/nonmangrove areas (Rahmanet al., 2013).As a response to the need for an alternative methodology, ourprevious studies have implemented the gas exchange method(Okimotoetal.,2007,2008),inrelationto Rhizophora stylosa inIsh-igaki, Japan and  Kandelia candel  in Thanh Hoa, Vietnam. The meth-od estimates photosynthetic carbon absorption in the canopy witha photosynthetic trait of light-response and canopy structureincluding leaf distribution and light penetration through the can-opy. Both studies have overcome some of the weaknesses of the‘traditional’ gas exchange method and then proposed an improvedversion as ‘‘gas exchange analysis method’’. According to the re-sults, it emerges that the accuracy in measuring carbon fixationcapacitycanbeenhancedbytemperaturemodification,andbycor-rectingcarbonvaluesof photosyntheticabsorptionandrespiratoryemission with a diurnal change model of temperature in eachmonth. In addition, the ‘‘growth curve analysis method’’ was alsoproposed as an alternative procedure. The growth curve approachshows the increment in tree biomass of the planted mangrovetrees, and also derives annual biomass increment values through-out the forest lifespan up to 23–28years of age (Gong and Ong,1995).With the gas exchange analysis method, photosynthetic car-bon absorption for natural mature trees of the  R. stylosa  was esti-mated to be 8.2MgCha  1 yr  1 (Okimoto et al., 2007). This valuewas similar to those values of 5.8–9.5MgCha  1 yr  1 for mixedmangrove species in Papua New Guinea, calculated by the tradi-tional gas exchange method (Bunt et al., 1979). In addition, thecarbon fixation capacity of 13.7MgCha  1 yr  1 for the 10year-old  K. candel  trees in Vietnam (Okimoto et al., 2008), was similarto those of 10.8MgCha  1 yr  1 for 15year-old  Rhizophora apicula-ta  trees in Thailand (Christensen, 1978) and 14.0MgCha  1 yr  1 for 10year-old  R. apiculata  trees in Malaysia (Ong, 1993), bothof which were calculated by the allometric method. This proxim-ity shows the validity of our emerging methodologies and indi-cates the possibility that our methods could supersede themethods traditionally used to account for the carbon fixationcapacity of mangrove trees.In this study, our developing methods to estimate carbon fix-ation capacity were applied to the well-managed monocultureforest of   R. apiculata  in Trat, Thailand. This is one of the feasibil-ity studies in methodological development, aiming to be validfor various mangrove species growing in South-East Asia. It isexpected that data collection in this feasibility study will en-hance accuracy estimation while developing the methodology.This study also proposes to estimate tree biomass not in dryweight units but both of surface area and volume by usingnon-destructive methods. The remaining mangrove trees andthe afforested/reforested trees at the field site are strictly pro-tected and cutting is prohibited, even for research. The non-destructive method to estimate biomass by surface area and vol-ume calculation only is therefore highly appropriate under theselocal conditions. 2. Materials and methods In this study, each component of the tree is expressed as leaf,branch, trunk and root. All these are collectively termed ‘‘organs’’in this paper.  2.1. Study site The study site was located at the mouth of River Weru in Tratprovince, Thailand (12  11 0 N, 102  34 0 E). The site is about 250kmeast of the capital Bangkok, close to the Cambodia border. The re-gionhasatropicalclimatewithannualaveragevaluesofprecipita-tion of 1942mm; temperature, 28.4  C; and a relative humidity of 78%.ThedryseasonlastsfromOctobertoMayandtherainyseasonfrom June to September.Our investigationwas carriedonsingle treespecimensof 3, 4, 5and 9year-old  R. apiculata  during March 9–13, 2001 and Novem-ber 20–25, 2003. This mangrove species is very widespread inSouth-East Asia. The trees are thinned by the RFD during the man-agement. Thecurrenttreeintervalsare1.0, 1.5, 2.0and4.0mfor3,4, 5 and 9year-old plantations and the tree density was 100, 44.4,25.0 and 6.3  10 2 treesha  1 , respectively. Heights of the repre-sentative single trees were 1.0, 1.7, 3.7 and 9.6m, respectively.With the official authorization of the RFD, the 4year-old singleabove-groundtreewascut andmeasurements takenonitsproper-ties of photosynthesis and respiration.  2.2. An estimation of net carbon fixation capacity by using analysismethods of gas exchange and growth curve The net carbon fixation capacity in 3, 4, 5 and 9year-old  R.apiculata  forests in Trat was estimated by the both methods of gas exchange and growth curve analysis and the details are de-scribed in Okimoto et al. (2008). In the gas exchange analysismethod, a response of PCER (Photosynthetic CO 2  Exchange Rate)to light and temperature was measured in leaves of the upper-and lower-layers of the canopy. Light extinction and distributionof the leaves in the canopy were measured to calculate carbonabsorption capacity of the canopy. Respiratory carbon emissionwas estimatedbymultiplyingthe RCER(RespiratoryCO 2  ExchangeRate) measured in a partial number of organs by the total amountof each organ in above-ground tree. Monthly averages based onwhole day absorption and emission of carbon was corrected withthediurnalvaluesoflightintensityandairtemperature.Anannualcarbon fixation capacity was estimated by integrating carbon bal-ance between the absorption and emission of carbon in eachmonth.Inthegrowthcurveanalysismethod,singletreebiomassof3,4,5 and 9year-old  R. apiculata  trees was measured in the units of both surface area and volume by using non-destructive method.Agrowthcurvewas calculatedbyplottingthevolumesat differenttree ages and the given maximumdry weight of tree, based on theformulation described in Okimoto et al. (2008).  2.3. Light response of PCER Steel towers (4–10m in height, depending on the canopyheight) were constructed in 5 and 9year-old forests to measureleaf photosynthesis and investigate the canopy structure. Lightresponses of PCER in full-expanded leaves of 4, 5 and 9year-oldtrees were measured by using a portable photosynthetic measure-ment system (LI-6400, Li-Cor). The leaves used for the measure-ment were located in the upper canopy for the 4year-old tree.For the 5 and 9year-old trees, leaves used for measurement werelocated in the vertical three layers of the canopy, i.e. the top, mid- Y. Okimoto et al./Forest Ecology and Management 310 (2013) 1016–1026   1017  dle and lower layers. The light intensity of photosynthetically ac-tive radiation (PAR) on leaf surfaces was automatically controlledin six steps in a descending order starting from 2000 to0 l molm  2 s  1 . During measurement, the leaf temperature wasmaintained at 25  C, vapor pressure deficit between the leaf andtheair (VpdL)was1.7±0.3kPaandCO 2  concentrationoftherefer-ence air was 370 l molmol  1 . At least three separate measure-ments were taken for each sample and then averaged to obtain afinal value.  2.4. Temperature response of PCER The temperature responses of PCER in the leaves of 4 and9year-oldtrees,locatedintheuppercanopy,weremeasuredusingthe LI-6400 instrument. Leaf temperature was changed from 20 to37  C on a random basis. In the measurements, VpdL, input CO 2 and PAR was 1.7±0.3kPa, 370 l molmol  1 and 1000 l molm  2 -s  1 , respectively.  2.5. Canopy structure with stratified clip technique The canopy structure of 5 and 9year-old forests was investi-gated using the stratified clip technique, in which a 1.0  1.73m 2 quadrate of the canopy was divided into a 0.5m thickness fromthecanopytoptotheforestfloor.Lightextinctionthroughthecan-opy was calculated by the percent relative irradiance between thelightincidentatthecanopytopandthelightintensityateachlayerinside the canopy. The light intensity was measured using a quan-tum sensor (LI-190SB, Li-Cor) and the leaf area index (LAI) wasestimated, following procedures outlined in an earlier report(Okimoto et al., 2008).  2.6. Measurements of above- and below-ground biomass The biomass of branch and trunk in 3, 4, 5 and 9year-old treeswas measured in the units of surface area and volume. The branchwas divided into four offshoot groups; first, second, third andfourth offshoots by using the same method of the previous study(Okimoto et al., 2008). Roots in the 4year-old tree were carefullycollected by excavation with an engine pump (SEG-25E, KoshinLtd.). They were divided into four offshoot groups; main root, firstlateralroot,secondlateralrootandthirdlateralroot.Themainrootwas separated into four part; upper-, upper-middle, middle-lowerand lower-part of the root with gradual increase in depth, whilethe lateral roots were separated into two parts; brown (B) andwhite (W), based on the color of the root surface. These biomasssamples were used for the RCER measurement. After extractingthese, the samples were dried at 115  C for more than a week.The dry weight, water content and wood density (weight per unitvolume) were estimated. The biomass in hectares was calculatedby multiplying the single tree biomass in dry weight by treedensity.  2.7. Temperature response of RCER in each organ Before the measurement, the samples were kept in a refrigera-tor at 8.0  C for one night to avoid measuring any excess CO 2  re-lease. The temperature response of RCER in trunk, branch androot was measured in the temperature range of 15–35  C at 5  Cinterval. The samples were treated similarly to the previous study(Okimoto et al., 2008) using an aluminum pan (18cm in diameterand 18cm in depth) with a propeller fan (MD825BM-12, TokudenCo., Ltd.) attached inside for mixing the air to maintain a constanttemperature. The RCER of the sample in the aluminum pan wasmeasured with a CO 2  analyzer (LI-800, Li-Cor) for at least 5minto obtain a stable data of linearly increasing CO 2  concentrations.  2.8. Estimation and temperature modification of absorption, emissionand net carbon fixation Photosynthetic carbon absorptionin the whole canopy was cal-culatedas anintegrationof PCERineachlayer. The PCERat agiventime during the day was corrected for the light intensity and tem-perature calculated by the equations described in Okimoto et al.(2008).Temperature modification in the gas exchange analysis methodfollowedthemethodsdescribedinOkimotoetal. (2008). Theaver-age diurnal temperature value of each month in the study site wascalculated by using the temperature data of the study site, Trat(2000–2001; Thailand Meteorological Department). The values of PCER and RCER varying in each time of the day and month werecalculated by substituting the diurnal temperature variation intothe regression formulas obtained from the temperature responsesof PCER and RCER. Those corrected carbon balances derive the netcarbon fixation with the temperature modification. By comparingboth values with and without the temperature variation, the effectof temperature modification in calculating net carbon fixationcould be determined. In the event of no temperature modification,the PCER and RCER were measured at an annual average tempera-ture of 27.3  C in the study site.  2.9. Annual biomass accumulation estimated by growth curve analysis In drawing a growth curve of single tree biomass, it is neces-sary to have actual tree biomass in different growth stages andthe maximum tree biomass in the mature period. The data of the tree biomass, starting with a very low initial value (i.e. thebiomass of a  propagule ), determines the shape of the growthcurve. A derivative value of the growth curve was calculatedand an annual biomass increment was calculated. It was impos-sible to cut trees down and measure the actual physical biomassin units of dry weight, because the site is designated a strictmangrove reserve by the Thai government. Tree biomass was in-stead estimated using a non-destructive method, by measuringthe diameters of the top and bottom part of each organ, as re-ported previously by Okimoto et al. (2008). The volume of theentire tree was calculated by successively accumulating the vol-umes of each above-ground organ. A growth curve was drawnusing the above-ground volumes of the 3, 4, 5 and 9year-oldtrees.Given the absence of literature on mangrove tree biomass esti-mation by volume, the maximum biomass was assumed based onthe biggest tree biomass among the four trees in this study, the9year-old 10.3  10  2 m 3 /tree. We assumed that the maximumbiomass was arbitrarily assumed to be about 1.5, 2.0 and 2.5 timeslarger than that of 9year-old tree, i.e. 15, 20 and 25  10  2 m 3 /tree, respectively, whichisinthefirstattemptatdrawingagrowthcurve in volume biomass. It is reported that mangroves reach theirclimax in 23–28years (Gong and Ong, 1995), thus the growthcurves were drawn assuming that the tree biomass reaches itsmaximum at 25years. 3. Results  3.1. Light response of PCER The light responses of PCER in the leaves of 4, 5 and 9year-oldtree are shown in Fig. 1. The maximum value of PCER (Pmax) was10.9, 12.4 and 13.2 l molCO 2  m  2 s  1 , respectively. The light re-sponsesofPCERwerewellfittedtomodifiedrectangularhyperbola(Baly, 1935): P   ¼  I  = ð a þ  b    I  Þ ð 1 Þ 1018  Y. Okimoto et al./Forest Ecology and Management 310 (2013) 1016–1026   where  P   ( l molCO 2  m  2 s  1 ) is PCER of individual leaves at lightintensity of   I   ( l molphotonm  2 s  1 ) and  a  and  b  are coefficientsto determine the convexity of the hyperbola.  3.2. Temperature response of PCER The temperature responses of PCER in the leaves of 4 and9year-old tree are shown in Fig. 2. The Pmax value acquired from4year-old tree was 13.1 l molCO 2  m  2 s  1 at 33  C leaf tempera-ture and that from the 9year-old tree was 13.2 l molCO 2  m  2 s  1 at 26  C leaf temperature. Although both of these are similar, theleaf temperature obtained for the Pmax value is quite different.The temperature responses fit a quadratic curve as follows (Fig. 2): 4 year-old tree  :  P   ¼  0 : 086  x 2 þ 5 : 37  x   70 : 6  ð 2 Þ 9 year-olds tree  :  P   ¼  0 : 033  x 2 þ 1 : 89  x   13 : 6  ð 3 Þ where P  ( l molCO 2  m  2 s  1 )isPCERofindividualleavesatleaftem-peratureof   x  (  C).Althoughthetemperatureresponsesintheleavesof the3and5year-oldtreewerenot measured, theywereassumedto be similar to that obtained for 4year-old tree.  3.3. Canopy structure and light profile in the canopy The productive structure in a 5 and 9year-old forest and lightprofile inside the forests is shown in Fig. 3. The leaf area index(LAI) of the 3, 4, 5 and 9year-old forest was 0.46, 2.13, 3.15 and4.75, respectively. The light extinction coefficient ( K  ), obtained byrelating the cumulative leaf area and logarithms of the relativelight intensities of each layer. The  K   value in the 5 and 9year-oldforest was 0.34 and 0.30, respectively. In the 3 and 4year-old for-est, light extinction inside the forest could be disregarded becauseall single trees in the study site were independent and without thecompetition of neighboring trees for growth.  3.4. Above- and below-ground biomass The biomass of each organ in a single tree of 3, 4, 5 and 9year-old was calculated by its surface area and volume (Fig. 4). Totalsurface area in a single tree of 3, 4, 5 and 9year-old was 0.1, 0.6,2.0 and 19.8m 2 . While, the total volume of each tree was 0.1,0.1, 0.6and10.3  10  2 m 3 . Inthebiomassas estimatedbysurfacearea, a ratio of the branch to the total single trees of 3, 4, 5 and9year-old was 51.4%, 76.1%, 73.4% and 71.4%, respectively, whichwas similar at trees of different ages. The ratio in volumes was dif- ; Upper layer ; Middle layer ; Lower layerPAR (µmol photon m -2  s -1 )    P   h  o   t  o  s  y  n   t   h  e   t   i  c   C   O    2   e  x  c   h  a  n  g  e  r  a   t  e  ;   P   C   E   R   (  µ  m  o   l  m   -   2    s   )   -   1 9 year-old tree P=I/(15.2+0.09·I)P=I/(21.5+0.10·I)P=I/(6.36+0.22·I) -4 -2 0 2 4 6 8 1012140 400800120016002000 5 year-old tree -4 -2 0 2 4 6 8 101214 P=I/(56.3+0.10·I)P=I/(3.64+0.06·I)P=I/(4.85+0.28·I) -4 -2 0 2 4 6 8 101214 4 year-old tree P=I/(10.4+0.07·I) Fig. 1.  Light response of photosynthetic CO 2  exchange rate (PCER) measured in theleaveslocatedintheuppercanopyof4year-oldandinthreelayersofthecanopyof 5 and 9 year-old  Rhizophora apiculata  grown in Trat, Thailand. They were measuredat 25  C leaf temperature. PAR in  Y  -axis shows photosynthetically active radiation. Leaf temperature (ºC) 0 2 4 6 8 10121425303540 P = -0.086x 2 + 5.37x - 70.6 0 2 4 6 8 101214152025303540 P = -0.033x 2 + 1.89x – 13.6 9 year-old tree 4 year-old tree    P   h  o   t  o  s  y  n   t   h  e   t   i  c   C   O    2   e  x  c   h  a  n  g  e  r  a   t  e  ;   P   C   E   R   (  µ  m  o   l  m   -   2    s   )   -   1 Fig. 2.  Temperature response of photosynthetic CO 2  exchange rate (PCER) mea-sured in the leaves of 4 and 9 year-old  Rhizophora apiculata  tree in Trat, Thailand. Aquadratic curve in the graph was a regression of temperature response of PCER.They were measured at 1000mmolm  2 s  1 of PAR. Vertical bars in the graph for 9year-old tree show standard deviation ( n  =5). Y. Okimoto et al./Forest Ecology and Management 310 (2013) 1016–1026   1019  ferent, calculated as 21.0, 50.3, 39.2 and 30.3% in the 3, 4, 5 and9year-old trees, respectively.Tocalculatethedryweightoftreebiomassatdifferentages,thewood density obtained from samples of the 4year-old tree (0.510and 0.488gcm  3 for trunk and branch, respectively, Table 1) wasmultiplied by the calculated value of tree biomass in volume. Thecalculated tree biomass of 3, 4, 5 and 9year-old was 0.1, 0.3, 1.2and 20.1kgCtrunk  1 , respectively. The above-ground tree bio-mass in hectares was 1.0, 1.3, 3.0 and 12.6MgCha  1 , respectively.Therootbiomassofthe4year-oldtreeshowninFig. 5indicates5.8m 2 in surface area and 1.9  10  2 m 3 in volume. Both of thesefigures are 10 times higher than those of the above-ground bio-mass. The root biomass of the 4year-old tree in dry weight wasmeasured as 2.5kgCtrunk  1 , calculated using the wood densityof root (0.329gcm  3 , Table 1). T/R ratio (the ratio of total weightof above-ground biomass to dry weight of the root) was 0.114.  3.5. Temperature response of RCER in each organ Temperature responses of RCER measured in trunk and branchof4year-oldtreeareshowninFig.6. TheRCERvaluesmeasuredinthe temperature range of 15–35  C were 5.8–18.6 l molCO 2  m  2 -s  1 for trunk and 1.1–17.9 l molCO 2  m  2 s  1 for branch, respec-tively. The RCER values observed in the trunk and the brown partof first branch are slightly higher. The temperature response of RCER is well regressed with an exponential equation and eachparameter of the equation shown in Table 2. Leaf area (m 2  / m 2  quadrate) Relative irradiance (%) 0 102030405060708090100 LAI = 3.14 K = 0.34 0-5050-100100-150150-200200-250 Leaf area (m 2  / 1.0×1.0 m 2  quadrate)    D  e  p   t   h   i  n   t   h  e  c  a  n  o  p  y   (  c  m   ) Relative irradiance (%) 350-400300-350250-300200-250150-200100-15050-1000-500 2.0 4.0 6.0 8.0 0 102030405060708090100 LAI = 4.11 K = 0.30 Leaf area Relative irradiance 00.20.40.60.81.01.21.41.6 5 year-old 9 year-old    D  e  p   t   h   i  n   t   h  e  c  a  n  o  p  y   (  c  m   ) Fig. 3.  Leaf distribution and light profile in 5 and 9 year-old  Rhizophora apiculata forest in Trat, Thailand. LAI and K in the graph show leaf area index and a lightextinction coefficient, respectively. Fig. 4.  Surface area and volume of trunk and each offshot of branch in 3, 4, 5 and 9year-old trees of   Rhizophora apiculata  in Trat, Thailand. Branches were divided intofour offshoot groups; first, second, third, and fourth offshoots. ‘‘B’’ and ‘‘G’’ in thegraph mean surface color of each part, i.e. lignified brown (B) and non-lignifiedgreen (G), respectively.  Table 1 Properties of quantitative characteristics of the representative part of 4 year-old Rhizophora apiculata  tree in Trat, Thailand. Surfacearea (cm 2 )Volume(cm 3 )Freshweight(gFW)Dry weight(gDW)Wooddensity(gcm  3 ) Trunk B 350 172 219 105 0.610G 334 88 104 36 0.409 Branch 1st B 329 142 197 95 0.6691st G 287 67 88 30 0.4482nd B 395 84 95 42 0.5002nd G 681 106 142 41 0.3873rd B 248 37 49 21 0.5683rd G 484 54 93 28 0.5194th B 140 18 20 7.8 0.4334th G 436 46 66 19 0.4135th B 111 12 19 5.4 0.450 Main root  Upper 132 116 147 68 0.586Upper-middle 214 293 389 171 0.584Middle-lower 171 187 179 74 0.396Lower 138 127 112 37 0.291 Lateral root  lst B 681 423 377 119 0.2812nd B 572 156 128 30 0.1922nd W 1615 1190 216 27 0.0233rd W large 170 18 17 1.9 0.1063rd W small 167 18 51 9.1 0.5061020  Y. Okimoto et al./Forest Ecology and Management 310 (2013) 1016–1026 
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